





import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_   :      List[Any]			=   logging.get_logger(__name__)



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Dict:
		__lowercase											=     OrderedDict()
		for key, value in state_dict.items():
				if key.startswith('module.encoder'							):
						__lowercase											=     key.replace('module.encoder'						,   'glpn.encoder'							)
				if key.startswith('module.decoder'							):
						__lowercase											=     key.replace('module.decoder'						,   'decoder.stages'							)
				if "patch_embed" in key:
						# replace for example patch_embed1 by patch_embeddings.0
						__lowercase											=     key[key.find('patch_embed'							) + len('patch_embed'							)]
						__lowercase											=     key.replace(F"patch_embed{idx}"						,   F"patch_embeddings.{int(_lowerCamelCase							)-1}"							)
				if "norm" in key:
						__lowercase											=     key.replace('norm'						,   'layer_norm'							)
				if "glpn.encoder.layer_norm" in key:
						# replace for example layer_norm1 by layer_norm.0
						__lowercase											=     key[key.find('glpn.encoder.layer_norm'							) + len('glpn.encoder.layer_norm'							)]
						__lowercase											=     key.replace(F"layer_norm{idx}"						,   F"layer_norm.{int(_lowerCamelCase							)-1}"							)
				if "layer_norm1" in key:
						__lowercase											=     key.replace('layer_norm1'						,   'layer_norm_1'							)
				if "layer_norm2" in key:
						__lowercase											=     key.replace('layer_norm2'						,   'layer_norm_2'							)
				if "block" in key:
						# replace for example block1 by block.0
						__lowercase											=     key[key.find('block'							) + len('block'							)]
						__lowercase											=     key.replace(F"block{idx}"						,   F"block.{int(_lowerCamelCase							)-1}"							)
				if "attn.q" in key:
						__lowercase											=     key.replace('attn.q'						,   'attention.self.query'							)
				if "attn.proj" in key:
						__lowercase											=     key.replace('attn.proj'						,   'attention.output.dense'							)
				if "attn" in key:
						__lowercase											=     key.replace('attn'						,   'attention.self'							)
				if "fc1" in key:
						__lowercase											=     key.replace('fc1'						,   'dense1'							)
				if "fc2" in key:
						__lowercase											=     key.replace('fc2'						,   'dense2'							)
				if "linear_pred" in key:
						__lowercase											=     key.replace('linear_pred'						,   'classifier'							)
				if "linear_fuse" in key:
						__lowercase											=     key.replace('linear_fuse.conv'						,   'linear_fuse'							)
						__lowercase											=     key.replace('linear_fuse.bn'						,   'batch_norm'							)
				if "linear_c" in key:
						# replace for example linear_c4 by linear_c.3
						__lowercase											=     key[key.find('linear_c'							) + len('linear_c'							)]
						__lowercase											=     key.replace(F"linear_c{idx}"						,   F"linear_c.{int(_lowerCamelCase							)-1}"							)
				if "bot_conv" in key:
						__lowercase											=     key.replace('bot_conv'						,   '0.convolution'							)
				if "skip_conv1" in key:
						__lowercase											=     key.replace('skip_conv1'						,   '1.convolution'							)
				if "skip_conv2" in key:
						__lowercase											=     key.replace('skip_conv2'						,   '2.convolution'							)
				if "fusion1" in key:
						__lowercase											=     key.replace('fusion1'						,   '1.fusion'							)
				if "fusion2" in key:
						__lowercase											=     key.replace('fusion2'						,   '2.fusion'							)
				if "fusion3" in key:
						__lowercase											=     key.replace('fusion3'						,   '3.fusion'							)
				if "fusion" in key and "conv" in key:
						__lowercase											=     key.replace('conv'						,   'convolutional_layer'							)
				if key.startswith('module.last_layer_depth'							):
						__lowercase											=     key.replace('module.last_layer_depth'						,   'head.head'							)
				__lowercase											=     value

		return new_state_dict



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    Union[str, Any]:





		for i in range(config.num_encoder_blocks							):
				for j in range(config.depths[i]							):
						# read in weights + bias of keys and values (which is a single matrix in the original implementation)
						__lowercase											=     state_dict.pop(F"glpn.encoder.block.{i}.{j}.attention.self.kv.weight"							)
						__lowercase											=     state_dict.pop(F"glpn.encoder.block.{i}.{j}.attention.self.kv.bias"							)
						# next, add keys and values (in that order) to the state dict
						__lowercase											=     kv_weight[
						    : config.hidden_sizes[i], :
						]
						__lowercase											=     kv_bias[: config.hidden_sizes[i]]
						__lowercase											=     kv_weight[
						    config.hidden_sizes[i] :, :
						]
						__lowercase											=     kv_bias[config.hidden_sizes[i] :]



def  SCREAMING_SNAKE_CASE				(      )		->    Optional[int]:
		__lowercase											=     "http://images.cocodataset.org/val2017/000000039769.jpg"
		__lowercase											=     Image.open(requests.get(_lowerCamelCase						,   stream=_lowerCamelCase							).raw							)

		return image



@torch.no_grad()
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case=False						,   snake_case=None							)		->    Any:
		__lowercase											=     GLPNConfig(hidden_sizes=[64, 128, 320, 512]						,   decoder_hidden_size=64						,   depths=[3, 8, 27, 3]							)

		# load image processor (only resize + rescale)
		__lowercase											=     GLPNImageProcessor()

		# prepare image
		__lowercase											=     prepare_img()
		__lowercase											=     image_processor(images=_lowerCamelCase						,   return_tensors='pt'							).pixel_values

		logger.info('Converting model...'							)

		# load original state dict
		__lowercase											=     torch.load(_lowerCamelCase						,   map_location=torch.device('cpu'							)							)

		# rename keys
		__lowercase											=     rename_keys(_lowerCamelCase							)

		# key and value matrices need special treatment
		read_in_k_v(_lowerCamelCase						,   _lowerCamelCase							)

		# create HuggingFace model and load state dict
		__lowercase											=     GLPNForDepthEstimation(_lowerCamelCase							)
		model.load_state_dict(_lowerCamelCase							)
		model.eval()

		# forward pass
		__lowercase											=     model(_lowerCamelCase							)
		__lowercase											=     outputs.predicted_depth

		# verify output
		if model_name is not None:
				if "nyu" in model_name:
						__lowercase											=     torch.tensor(
						    [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]]							)
				elif "kitti" in model_name:
						__lowercase											=     torch.tensor(
						    [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]							)
				else:
						raise ValueError(F"Unknown model name: {model_name}"							)

				__lowercase											=     torch.Size([1, 480, 640]							)

				assert predicted_depth.shape == expected_shape
				assert torch.allclose(predicted_depth[0, :3, :3]						,   _lowerCamelCase						,   atol=1E-4							)
				print('Looks ok!'							)

		# finally, push to hub if required
		if push_to_hub:
				logger.info('Pushing model and image processor to the hub...'							)
				model.push_to_hub(
				    repo_path_or_name=Path(_lowerCamelCase						,   _lowerCamelCase							)						,   organization='nielsr'						,   commit_message='Add model'						,   use_temp_dir=_lowerCamelCase						,   )
				image_processor.push_to_hub(
				    repo_path_or_name=Path(_lowerCamelCase						,   _lowerCamelCase							)						,   organization='nielsr'						,   commit_message='Add image processor'						,   use_temp_dir=_lowerCamelCase						,   )


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   argparse.ArgumentParser()

		parser.add_argument(
		    '''--checkpoint_path''',
		    default=None,
		    type=str,
		    help='''Path to the original PyTorch checkpoint (.pth file).''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
		)
		parser.add_argument(
		    '''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
		)
		parser.add_argument(
		    '''--model_name''',
		    default='''glpn-kitti''',
		    type=str,
		    help='''Name of the model in case you\'re pushing to the hub.''',
		)
		SCREAMING_SNAKE_CASE_   :      Dict			=   parser.parse_args()
		convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






import math




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     int)   ->       str:






							'''simple docstring'''





							__UpperCamelCase      :  Union[str, Any]       =   0
							__UpperCamelCase      :  int       =   0
							while num > 0:
														__UpperCamelCase      :  List[Any]       =   num % 8
														__UpperCamelCase      :  Tuple       =   octal + (remainder * math.floor(math.pow(10		,    _lowerCamelCase)))
														counter += 1
														__UpperCamelCase      :  Optional[Any]       =   math.floor(num / 8)  # basically /= 8 without remainder if any
														# This formatting removes trailing '.0' from `octal`.
							return F'0o{int(_lowerCamelCase)}'




def       _SCREAMING_SNAKE_CASE   (						)   ->       None:






							'''simple docstring'''





							print("\n2 in octal is:")
							print(decimal_to_octal(2))  # = 2
							print("\n8 in octal is:")
							print(decimal_to_octal(8))  # = 10
							print("\n65 in octal is:")
							print(decimal_to_octal(65))  # = 101
							print("\n216 in octal is:")
							print(decimal_to_octal(216))  # = 330
							print("\n512 in octal is:")
							print(decimal_to_octal(512))  # = 1000
							print("\n")


if __name__ == "__main__":
		main()